The present invention relates generally to machine vision vehicle wheel alignment systems configured to measure the locations and orientation of the wheels of a vehicle in a three dimensional coordinate system, and more particularly, to methods for processing acquired images of the vehicle wheel assemblies, from which vehicle wheel alignment measurements are determined.
Aligning vehicle wheels within specific tolerances is important for optimal performance, handling, and control of the vehicle, and for consistent wear of the tires. Alignment is performed primarily by adjusting the camber, caster, toe, and steering axis inclination angles of the vehicle suspension system. As part of calculating the alignment angles for the vehicle, the position and orientation (pose) of the wheel assemblies must be determined.
A typical target-based machine vision vehicle wheel alignment system 10, shown in FIG. 1, such as the Series 811 wheel alignment system configured with the DSP600 sensors manufactured by Hunter Engineering Co. of Bridgeton, Mo. consists of a console unit 12, imaging sensors 22, and a set of alignment targets 24. The console unit 12 contains a processor or computer 14 configured with image processing and vehicle wheel alignment software applications, and may incorporate various communication and operator interfaces, including a keyboard 18, a mouse, a printer, and a display device 16. The imaging sensors 22 are coupled to the computer 14, and alignment targets 24 are disposed in the field of view of the imaging sensors 22, typically mounted to the wheels 100 of a vehicle undergoing an alignment inspection.
Commonly, to view the left and right sides of a vehicle, one or more imaging sensors 22 are disposed on opposite sides of the vehicle, each having a field of view encompassing one or more wheels 100 of the vehicle. In alternative configurations, two imaging sensors 22 are provided on each side of the vehicle, each having a field of view encompassing a single vehicle wheel 100, i.e. a left front, left rear, right front, and right rear wheel, respectively. To facilitate vehicle wheel alignment, alignment targets 24 are mounted on the vehicle wheels 100, and observed by the imaging sensors 22. The alignment targets 24 preferably have predetermined control features 26 disposed on a target surface 25 which are identified in images obtained by the imaging sensors 22, and which facilitate a determination of the position and orientation (pose) of the alignment targets 24, as set forth in U.S. Pat. No. 6,064,750. The image processing may take place in the imaging sensor modules 22, in an interface computer, or in the console computer 14.
The pose of each wheel 100 is determined by estimated the position and orientation (pose) of the attached alignment target 24 from an acquired image. A mathematical model of the alignment target 24 is employed, containing the three-dimensional coordinates of visible features 26 on the target surface relative to a target coordinate system. An estimate of the target position in a coordinate system of the observing imaging sensor is chosen by searching for the prospective target position that minimizes the differences between the image plane locations of target features observed in the image and the prospective image plane locations of target features that should result from observing the alignment target 24 at the prospective target position. The prospective image plane coordinates of a target feature 26 are determined by applying a mathematical model of the imaging sensor to the prospective three-dimensional location of the target features 26. The prospective three-dimensional location of a target feature 26 is determined by combining the prospective target position with the target model information.
Once the position and orientation of each alignment target 24 is determined, the pose of the associated vehicle wheel 100 can be determined, and correspondingly, the various vehicle wheel alignment angle measurements may be either determined or calculated using known mathematical techniques. These angles typically include camber, caster, and toe angles for each vehicle wheel 100, the vehicle centerline, and the vehicle rear thrust line.
Some machine vision systems 10 do not use predefined alignment targets 24, and are configured to identify predetermined geometric features directly on the surfaces of the wheel or tire of each wheel assembly 100, such as projected light stripes or the circular wheel rim. These systems may utilize observed distortion of the geometric features, or changes in positions thereof, to determine positions and orientations of the associated vehicle wheels 100.
The next generation of machine vision vehicle wheel alignment systems may be implemented without requiring alignment targets 24 to be mounted to the vehicle wheels 100 or the use of predetermined geometric features. Instead, the wheel assembly 100 itself, consisting of the tire and supporting wheel rim, may function as an alignment target in images acquired by the imaging sensors 22. The processing system 12 may then use these images to calculate some or all of the six degrees of freedom (6-DOF) components consisting of positional data (X, Y, and Z coordinates) and rotational data (rotation about the X, Y, and Z axis), otherwise known collectively as pose and individually as pose components, associated with each wheel assembly 100 in an image. Using some or all of the calculated six degrees of freedom components, or pose information, various vehicle wheel alignment measurements may be determined using known mathematical techniques. The precision of the 6-DOF components is limited by how accurately identifiable features of interest can be located in the image of the wheel assembly 100.
While machine vision wheel alignment systems which utilize alignment targets 24 have the advantage of utilizing highly accurate, predefined target patterns to produce exact measurements, independent of each vehicle undergoing an alignment procedure, target-less machine vision alignment systems do not. The random features of interest associated with the vehicle wheel assemblies which are observable in the acquired images will likely vary from vehicle-to-vehicle, and from wheel assembly to wheel assembly. Accordingly, there is a need for image processing methods for use in machine-vision vehicle wheel alignment systems which are sufficiently robust to accommodate the wide range of features of interest which may be observed during target-less vehicle wheel alignment procedures.